Validation of National Early Warning Score for predicting 30-day mortality after rapid response system activation in Japan

Takaki Naito; Kuniyoshi Hayashi; Hsiang-Chin Hsu; Kazuhiro Aoki; Kazuma Nagata; Masayasu Arai; Taka-Aki Nakada; Shinichiro Suzaki; Yoshiro Hayashi; Shigeki Fujitani; In‐Hospital Emergency Study Group

doi:10.1002/ams2.666

Validation of National Early Warning Score for predicting 30-day mortality after rapid response system activation in Japan

Acute Med Surg. 2021 May 15;8(1):e666. doi: 10.1002/ams2.666. eCollection 2021 Jan-Dec.

Affiliations

¹ Department of Emergency and Critical Care Medicine St. Marianna University School of Medicine Kanagawa Japan.
² Graduate School of Public Health St. Luke's International University Tokyo Japan.
³ Department of Emergency Medicine National Cheng Kung University Tainan City Taiwan.
⁴ Department of Anesthesiology and Intensive Care Medicine St. Luke's International Hospital Tokyo Japan.
⁵ Department of Respiratory Medicine Kobe City Medical Center General Hospital Hyogo Japan.
⁶ Department of Anesthesiology Kitasato University School of Medicine Kanagawa Japan.
⁷ Department of Emergency and Critical Care Medicine Chiba University Graduate School of Medicine Chiba Japan.
⁸ Department of Emergency and Critical Care Medicine Japanese Red Cross Musashino Hospital Tokyo Japan.
⁹ Department of Intensive Care Medicine Kameda Medical Center Chiba Japan.

Abstract

Aim: Although rapid response systems (RRS) are used to prevent adverse events, Japan reportedly has low activation rates and high mortality rates. The National Early Warning Score (NEWS) could provide a solution, but it has not been validated in Japan. We aimed to validate NEWS for Japanese patients.

Methods: This retrospective observational study included data of 2,255 adult patients from 33 facilities registered in the In-Hospital Emergency Registry in Japan between January 2014 and March 2018. The primary evaluated outcome was mortality rate 30 days after RRS activation. Accuracy of NEWS was analyzed with the correlation coefficient and area under the receiver operating characteristic curve. Prediction weights of NEWS parameters were then analyzed using multiple logistic regression and a machine learning method, classification and regression trees.

Results: The correlation coefficient of NEWS for 30-day mortality rate was 0.95 (95% confidence interval [CI], 0.88-0.98) and the area under the receiver operating characteristic curve was 0.668 (95% CI, 0.642-0.693). Sensitivity and specificity values with a cut-off score of 7 were 89.8% and 45.1%, respectively. Regarding prediction values of each parameter, oxygen saturation showed the highest odds ratio of 1.36 (95% CI, 1.25-1.48), followed by altered mental status 1.23 (95% CI, 1.14-1.32), heart rate 1.21 (95% CI, 1.09-1.34), systolic blood pressure 1.12 (95% CI, 1.04-1.22), and respiratory rate 1.03 (95% CI, 1.05-1.26). Body temperature and oxygen supplementation were not significantly associated. Classification and regression trees showed oxygen saturation as the most heavily weighted parameter, followed by altered mental status and respiratory rate.

Conclusions: National Early Warning Score could stratify 30-day mortality risk following RRS activation in Japanese patients.

Keywords: In‐hospital cardiac arrest; machine learning; medical emergency team; national early warning score; rapid response system.